Embodiments of the present disclosure generally relate to systems and methods for detecting faults or failures within one or more operative sub-systems of a vehicle.
Various vehicles include numerous electronics, hardware, and other sub-systems that are used during operation of the vehicles. For example, a typical aircraft includes numerous sub-systems (such as flight control systems, radar systems, air conditioning units, air intakes, blowers, electronics, and the like) positioned throughout the aircraft. At least some of the sub-systems may be vital to the performance or intended mission of the aircraft. For example, an airplane may include radar electronics in a forward portion of the fuselage and hydraulic and pneumatic systems throughout the fuselage. Various military aircraft include a broad suite of systems and electronics, many of which are mission and/or flight critical systems.
Operative sub-systems of a vehicle may be monitored over time to determine whether or not they are functioning properly. Physics or model-based methods may be used to monitor an operative sub-system. These types of methods attempt to understand the physical model of the operative sub-system and determine expected values in normal operating conditions. An alert may be output if sensor readings deviate from the expected values. For example, active channels of an aircraft elevator reactive controller may be monitored through the use of rules generated by a logic table that includes or is correlated with expected values. If a fault is detected, the model-based method may be directed to output a sequence of action items.
In another model-based method, state space models along with Kalman filtering are used to monitor an operative sub-system. However, in such a model-based method, a large number of channels are monitored, which may increase the complexity and cost of the systems. Further, when a component is changed or upgraded, new models typically need to be generated.
Operative sub-systems may be also be monitored through pattern recognition methods. However, pattern recognition methods for detecting faults typically require large amounts of training data in order to accurately detect faults. Further, when a system that is being monitored undergoes a change (such as a system upgrade), the pattern recognition model or method needs to be retrained.
Accordingly, a need exists for a system and method that efficiently detects faults within one or more operative sub-systems of a vehicle.